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Turing proposed that a human evaluator would judge natural language conversations between a human and a machine that is designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation is a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel such as a computer keyboard and screen so that the result would not be dependent on the machine's ability to render words as speech. If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test.
The legal industry and profession are on the cusp of a revolution in the practice of law led by the adoption of artificial intelligence (AI) computers – in particular by in-house lawyers. Part I of this series defined artificial intelligence and how it works. Part II tackled the question of whether AI will replace lawyers. This installment takes a closer look at the many practical use cases for and benefits of AI in legal departments.
Before identifying the many ways AI might be used by legal departments, it is useful to understand how companies in general are using AI. One thing to underscore for both is the stark fact as to why AI is popular with businesses: Technology doesn’t get tired, sleep in, call in sick, or take vacations, and it doesn’t need breaks for food or when nature calls. Technology just keeps working and working and working. That is a huge benefit to any company – and their legal department.
The AI use most in-house lawyers are familiar with is for purposes of e-discovery (though I promise you I am not going to make you read another article on e-discovery). Originally, the AI use here was pretty simple – looking for keywords in megabytes of data. Doing this saved an amazing amount of time and money that would otherwise be spent paying lawyers to find documents that might be relevant. Later, AI was used to eliminate duplicate documents and connect strings of emails, again doing in minutes what would take days if done by people. And finally, AI is now capable of searching documents for context, concepts and tone with what is known as predictive coding, going far beyond simple keyword searches. Predictive coding is even being used as a tool in internal compliance investigations – to sift through mounds of data in minutes or hours.
Use for e-discovery is great and the benefits of AI in terms of time and cost are apparent. But is that it for in-house lawyers? The short answer is no. The longer answer is definitely no. AI has tremendous possibilities in the legal field and we are seeing many advances on that front already. While a number of outside law firms are in the process of developing uses for AI, the biggest potential impact comes from how AI is used by in-house legal departments. This is because the incentive for in-house legal departments to find the lowest cost way to do things is greater than the incentive for law firms to find ways to offer lower priced services (i.e., quantity of billable hours vs. quality of a billable hour). With that in mind, here is a list of some of the things AI can do (or is coming) for in-house legal departments that will disrupt and benefit the legal market:
1. Due diligence reviews. If you have ever been involved in a due diligence review for a corporate transaction you know that it typically involves a bunch of lawyers going through documents (hard copy or in an e-room) looking for litigation issues, key contract clauses (e.g., change of control, assignment, MFN, termination, etc.), corporate governance, intellectual property, etc. Generally, it takes many hands (usually outside counsel) and many hours/days to complete. There are now tools that can automate this process using AI, including finding specific legal concepts and generating written reports about what was found.
2. Prepare contracts. The “Holy Grail” for in-house lawyers who draft contracts is the ability to create and use a form agreement, i.e., one that has standard terms and conditions and requires (or allows) limited changes/customization. Form contracts are huge time-savers and allow the company to have a consistent set of agreements. There are AI tools that can create contracts, using whatever set of parameters the legal department feels important. Moreover, the tool can be set up as “self-service” for clients, i.e., the client can log onto the system, select the type of contract they need, enter in a few variables, and the system will produce a standard form agreement ready to go. The legal department can decide how much it wants to be involved in the creation process, i.e., a quick review of all contracts generated from the system (or at least a sign-off process) or only see contracts of a certain nature or if the client needs something nonstandard.
3. Contract management. A true pain in the neck of every in-house lawyer is a contract storage and management system – the place where contracts can be stored and then managed based on the terms of the agreement. For example, what is the termination date and when does notice of renewal need to go out? Is there a price escalation provision and when does it allow the price to increase and what type of notice, if any, needs to go out? Historically, this is all done manually. Someone either creates a spreadsheet and tracks everything by hand, or enters the data manually into a system that manages the key terms and dates automatically. AI has progressed to the point where the entry of the key information (terms, dates and other information) can be done through technology – without the need for human intervention other than initial setup and fine-tuning. In fact, contract management – including signature process – can be managed as part of the same tools that create the contracts, meaning an automated process from start to signature to storage. An additional problem with even considering a contract management tool is the work needed to review all of the existing contracts and inputting that data into the new database. AI can review your entire contract database (from whatever disparate sources are used to hold contracts) and analyze and organize those agreements in a manner that would take a team of people months and months to complete. Moreover, tools now exist that can review your entire contract database and manage risk, while ensuring consistent oversight and consistency among your contracts.
4. Legal spend/Legal operations analysis. While some in-house legal departments are still using paper invoices, most are moving to e-billing systems. There is a wealth of information in the e-billing system but, unfortunately, many in-house lawyers are not good at extracting the information in a useful manner. AI is solving that problem, providing the capability to analyze what work was done, how it aligns with other work done by that firm, how the work/efficiency compares with work provided by other firms engaged by the company, and how the work/efficiency compares to the market generally. Imagine being armed with that information the next time you want to discuss billing rates with your law firms. Additionally, there are operations tools that can provide you with reports and dashboards showing what type of work is coming into legal, who is working on it, how long it is taking, and what is the risk profile of that work. It also can assign the work to the right lawyers (in-house or outside) and provide case management tools.
5. Litigation analysis. There is an amazing amount of data in the U.S. court system’s public records. Opinions and orders of courts, jury verdicts, and other valuable information is generally fully available. Wouldn’t it be great to be able to search all of that data and be able to predict the outcome of litigation? Well, no surprise, AI is already providing a solution here as well. It will soon be possible to compare the facts of your case to other cases already decided by a court (or courts) and get a predictor of how your case will fare. A constant question in-house lawyers get about litigation is, “What are our odds of winning?” Such a tool could provide some much-needed analytics behind what is often a gut call.
6. “Wrong doing” detection. You may remember the 2002 Tom Cruise movie called “Minority Report.” Mr. Cruise played a member of the PreCrime police, a group that stopped crimes before they occurred because they had access to information that told them the crimes were about to happen. Granted, the information was provided by three clairvoyant mutant humans floating in a milk bath, but I’m going to leave that part out for purposes of this discussion. Taking the concept of predictive coding further, it is now possible to utilize AI to search company records (documents, emails, and even unstructured data) to detect bad behavior before it can bubble to the surface. AI is being used to sniff out bribery, fraud, compliance issues, even potential litigation – all based on the content of the company’s own documents and data. AI can summarize conversations and the ideas discussed, sniff out the use of code words, note the frequency of the communications, and even identify the mood of the speakers.
7. Legal research. Another use case is legal research. More often than not, in-house lawyers either shortchange the research process because they don’t want to spend the time or money to do a complete job, or pay a law firm to have a first- or second-year lawyer flail away on the question. AI will allow you to ask legal questions in plain language and get an answer back – an answer that includes researching regulations, caselaw, secondary sources and more. Moreover, it may be possible to use AI as a FAQ service that can answer basic legal, HR and compliance questions from your in-house clients, yet be smart enough to know when to defer the answer to a live attorney. The key here is that not only can the use of AI save time and money, it provides in-house counsel with the one luxury that seems to be missing each and every day: the luxury of time to think about the problem and provide the best legal judgment and analysis.
Indeed, the future is now and the benefits of AI in a legal department are many. AI has arrived in terms of assisting lawyers to do things faster, better, and cheaper. The existing technology may be limited for now, but the possibilities are intriguing and the availability, quality, and price will all soon come together in products that are just too useful for in-house legal departments to resist. AI is currently touching many in-house workflows – legal research, contract development and many others to come – forever disrupting the legal market. In the final installment of this series, we will discuss what in-house lawyers should be doing now to take advantage of the benefits AI has to offer.